scholarly journals Robust Scale Adaptive Visual Tracking with Correlation Filters

2018 ◽  
Vol 8 (11) ◽  
pp. 2037 ◽  
Author(s):  
Chunbao Li ◽  
Bo Yang
Keyword(s):  
Visual Tracking ◽  
State Of The Art ◽  
Estimation Method ◽  
Color Naming ◽  
Target Object ◽  
Correlation Filter ◽  
Correlation Filters ◽  
Object Proposals ◽  
Benchmark Datasets ◽  
Candidate Object

Visual tracking is a challenging task in computer vision due to various appearance changes of the target object. In recent years, correlation filter plays an important role in visual tracking and many state-of-the-art correlation filter based trackers are proposed in the literature. However, these trackers still have certain limitations. Most of existing trackers cannot well deal with scale variation, and they may easily drift to the background in the case of occlusion. To overcome the above problems, we propose a Correlation Filters based Scale Adaptive (CFSA) visual tracker. In the tracker, a modified EdgeBoxes generator, is proposed to generate high-quality candidate object proposals for tracking. The pool of generated candidate object proposals is adopted to estimate the position of the target object using a kernelized correlation filter based tracker with HOG and color naming features. In order to deal with changes in target scale, a scale estimation method is proposed by combining the water flow driven MBD (minimum barrier distance) algorithm with the estimated position. Furthermore, an online updating schema is adopted to reduce the interference of the surrounding background. Experimental results on two large benchmark datasets demonstrate that the CFSA tracker achieves favorable performance compared with the state-of-the-art trackers.

scholarly journals Visual Tracking Based on Complementary Learners with Distractor Handling

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Suryo Adhi Wibowo ◽  
Hansoo Lee ◽  
Eun Kyeong Kim ◽  
Sungshin Kim
Keyword(s):  
Visual Tracking ◽  
Object Representation ◽  
Target Location ◽  
Target Object ◽  
Tracking Algorithm ◽  
Color Histogram ◽  
Correlation Filter ◽  
Visual Object ◽  
Visual Object Tracking ◽  
Benchmark Datasets

The representation of the object is an important factor in building a robust visual object tracking algorithm. To resolve this problem, complementary learners that use color histogram- and correlation filter-based representation to represent the target object can be used since they each have advantages that can be exploited to compensate the other’s drawback in visual tracking. Further, a tracking algorithm can fail because of the distractor, even when complementary learners have been implemented for the target object representation. In this study, we show that, in order to handle the distractor, first the distractor must be detected by learning the responses from the color-histogram- and correlation-filter-based representation. Then, to determine the target location, we can decide whether the responses from each representation should be merged or only the response from the correlation filter should be used. This decision depends on the result obtained from the distractor detection process. Experiments were performed on the widely used VOT2014 and VOT2015 benchmark datasets. It was verified that our proposed method performs favorably as compared with several state-of-the-art visual tracking algorithms.

scholarly journals Enhanced Bounding Box Estimation with Distribution Calibration for Visual Tracking

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8100
Author(s):  
Bin Yu ◽  
Ming Tang ◽  
Guibo Zhu ◽  
Jinqiao Wang ◽  
Hanqing Lu
Keyword(s):  
Visual Tracking ◽  
Large Scale ◽  
State Of The Art ◽  
Estimation Method ◽  
Target Object ◽  
Reference Information ◽  
Bounding Box ◽  
The Mean ◽  
Modulation Vector ◽  
Initial Target

Bounding box estimation by overlap maximization has improved the state of the art of visual tracking significantly, yet the improvement in robustness and accuracy is restricted by the limited reference information, i.e., the initial target. In this paper, we present DCOM, a novel bounding box estimation method for visual tracking, based on distribution calibration and overlap maximization. We assume every dimension in the modulation vector follows a Gaussian distribution, so that the mean and the variance can borrow from those of similar targets in large-scale training datasets. As such, sufficient and reliable reference information can be obtained from the calibrated distribution, leading to a more robust and accurate target estimation. Additionally, an updating strategy for the modulation vector is proposed to adapt the variation of the target object. Our method can be built on top of off-the-shelf networks without finetuning and extra parameters. It yields state-of-the-art performance on three popular benchmarks, including GOT-10k, LaSOT, and NfS while running at around 40 FPS, confirming its effectiveness and efficiency.

scholarly journals A Robust Visual Tracking Algorithm Based on Spatial-Temporal Context Hierarchical Response Fusion

Algorithms ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 8 ◽  
Author(s):  
Wancheng Zhang ◽  
Yanmin Luo ◽  
Zhi Chen ◽  
Yongzhao Du ◽  
Daxin Zhu ◽  
...  
Keyword(s):  
Visual Tracking ◽  
Correlation Filter ◽  
Temporal Context ◽  
Visual Object ◽  
Correlation Filters ◽  
Visual Object Tracking ◽  
Illumination Changes ◽  
Model Update ◽  
Benchmark Datasets ◽  
Hierarchical Features

Discriminative correlation filters (DCFs) have been shown to perform superiorly in visual object tracking. However, visual tracking is still challenging when the target objects undergo complex scenarios such as occlusion, deformation, scale changes and illumination changes. In this paper, we utilize the hierarchical features of convolutional neural networks (CNNs) and learn a spatial-temporal context correlation filter on convolutional layers. Then, the translation is estimated by fusing the response score of the filters on the three convolutional layers. In terms of scale estimation, we learn a discriminative correlation filter to estimate scale from the best confidence results. Furthermore, we proposed a re-detection activation discrimination method to improve the robustness of visual tracking in the case of tracking failure and an adaptive model update method to reduce tracking drift caused by noisy updates. We evaluate the proposed tracker with DCFs and deep features on OTB benchmark datasets. The tracking results demonstrated that the proposed algorithm is superior to several state-of-the-art DCF methods in terms of accuracy and robustness.

scholarly journals A compressed multiple feature and adaptive scale estimation method for correlation filter-based visual tracking

2018 ◽  
Vol 15 (1) ◽  
pp. 172988141775151 ◽  
Author(s):  
ZL Wang ◽  
BG Cai
Keyword(s):  
Visual Tracking ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
Color Naming ◽  
Correlation Filter ◽  
Data Set ◽  
Scale Estimation ◽  
Searching Strategy ◽  
Multiple Feature ◽  
Pixel Value

The core part of the popular tracking-by-detection trackers is the discriminative classifier, which distinguishes the tracked target from the surrounding environment. Correlation filter-based visual tracking methods have the advantage of computing efficiency over the traditional methods by exploiting the properties of circulant matrix in learning process, and the significant progress in efficiency has been achieved by making use of the fast Fourier transform at detection and learning stages. But most existing correlation filter-based approaches are mainly restricted to translation estimation, which are susceptible to drifting in long-term tracking. In this article, a compressed multiple feature and adaptive scale estimation method is presented, which uses multiple features, including histogram of orientation gradients, color-naming, and raw pixel value to further improve the stability and accuracy of translation estimation. And for the scale estimation, another correlation filter is trained, which uses the compressed histogram of orientation gradients and raw pixel value to construct a multiscale pyramid of the target, and the optimal scale is obtained by exhaustively searching. The translation and scale estimation are unified with an iterative searching strategy. Extensively experimental results on the benchmark data set of scale variation show that the performance of the proposed compressed multiple feature and adaptive scale estimation algorithm is competitive against state-of-the-art methods with scale estimation capabilities in terms of robustness and accuracy.

scholarly journals Adaptive Context-Aware and Structural Correlation Filter for Visual Tracking

2019 ◽  
Vol 9 (7) ◽  
pp. 1338 ◽  
Author(s):  
Bin Zhou ◽  
Tuo Wang
Keyword(s):  
Visual Tracking ◽  
High Performance ◽  
State Of The Art ◽  
Correlation Filter ◽  
Context Aware ◽  
Partial Occlusion ◽  
Structural Correlation ◽  
Background Clutter ◽  
Overall Performance ◽  
Fast Motion

Accurate visual tracking is a challenging issue in computer vision. Correlation filter (CF) based methods are sought in visual tracking based on their efficiency and high performance. Nonetheless, traditional CF-based trackers have insufficient context information, and easily drift in scenes of fast motion or background clutter. Moreover, CF-based trackers are sensitive to partial occlusion, which may reduce their overall performance and even lead to failure in tracking challenge. In this paper, we presented an adaptive context-aware (CA) and structural correlation filter for tracking. Firstly, we propose a novel context selecting strategy to obtain negative samples. Secondly, to gain robustness against partial occlusion, we construct a structural correlation filter by learning both the holistic and local models. Finally, we introduce an adaptive updating scheme by using a fluctuation parameter. Extensive comprehensive experiments on object tracking benchmark (OTB)-100 datasets demonstrate that our proposed tracker performs favorably against several state-of-the-art trackers.

scholarly journals Real-Time Visual Tracking with Variational Structure Attention Network

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4904 ◽  
Author(s):  
Yeongbin Kim ◽  
Joongchol Shin ◽  
Hasil Park ◽  
Joonki Paik
Keyword(s):  
Real Time ◽  
Visual Tracking ◽  
Boundary Effect ◽  
Online Training ◽  
Correlation Filter ◽  
Shape Distortion ◽  
Correlation Filters ◽  
Attention Network ◽  
Real Time Processing ◽  
Variational Structure

Online training framework based on discriminative correlation filters for visual tracking has recently shown significant improvement in both accuracy and speed. However, correlation filter-base discriminative approaches have a common problem of tracking performance degradation when the local structure of a target is distorted by the boundary effect problem. The shape distortion of the target is mainly caused by the circulant structure in the Fourier domain processing, and it makes the correlation filter learn distorted training samples. In this paper, we present a structure–attention network to preserve the target structure from the structure distortion caused by the boundary effect. More specifically, we adopt a variational auto-encoder as a structure–attention network to make various and representative target structures. We also proposed two denoising criteria using a novel reconstruction loss for variational auto-encoding framework to capture more robust structures even under the boundary condition. Through the proposed structure–attention framework, discriminative correlation filters can learn robust structure information of targets during online training with an enhanced discriminating performance and adaptability. Experimental results on major visual tracking benchmark datasets show that the proposed method produces a better or comparable performance compared with the state-of-the-art tracking methods with a real-time processing speed of more than 80 frames per second.

scholarly journals Robust Visual Tracking with Reliable Object Information and Kalman Filter

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 889
Author(s):  
Hang Chen ◽  
Weiguo Zhang ◽  
Danghui Yan
Keyword(s):  
Kalman Filter ◽  
Visual Tracking ◽  
State Of The Art ◽  
Online Training ◽  
Linear Interpolation ◽  
Correlation Filter ◽  
Target Information ◽  
Filter Methods ◽  
Tracking Process ◽  
Background Clutter

Object information significantly affects the performance of visual tracking. However, it is difficult to obtain accurate target foreground information because of the existence of challenging scenarios, such as occlusion, background clutter, drastic change of appearance, and so forth. Traditional correlation filter methods roughly use linear interpolation to update the model, which may lead to the introduction of noise and the loss of reliable target information, resulting in the degradation of tracking performance. In this paper, we propose a novel robust visual tracking framework with reliable object information and Kalman filter (KF). Firstly, we analyze the reliability of the tracking process, calculate the confidence of the target information at the current estimated location, and determine whether it is necessary to carry out the online training and update step. Secondly, we also model the target motion between frames with a KF module, and use it to supplement the correlation filter estimation. Finally, in order to keep the most reliable target information of the first frame in the whole tracking process, we propose a new online training method, which can improve the robustness of the tracker. Extensive experiments on several benchmarks demonstrate the effectiveness and robustness of our proposed method, and our method achieves a comparable or better performance compared with several other state-of-the-art trackers.

scholarly journals Spatio-temporal joint aberrance suppressed correlation filter for visual tracking

2021 ◽  
Author(s):  
Libin Xu ◽  
Pyoungwon Kim ◽  
Mengjie Wang ◽  
Jinfeng Pan ◽  
Xiaomin Yang ◽  
...  
Keyword(s):  
Visual Tracking ◽  
State Of The Art ◽  
Boundary Effect ◽  
Correlation Filter ◽  
Star Model ◽  
Unified Framework ◽  
Alternating Direction ◽  
Background Clutter ◽  
Discriminative Correlation Filter ◽  
Spatio Temporal

AbstractThe discriminative correlation filter (DCF)-based tracking methods have achieved remarkable performance in visual tracking. However, the existing DCF paradigm still suffers from dilemmas such as boundary effect, filter degradation, and aberrance. To address these problems, we propose a spatio-temporal joint aberrance suppressed regularization (STAR) correlation filter tracker under a unified framework of response map. Specifically, a dynamic spatio-temporal regularizer is introduced into the DCF to alleviate the boundary effect and filter degradation, simultaneously. Meanwhile, an aberrance suppressed regularizer is exploited to reduce the interference of background clutter. The proposed STAR model is effectively optimized using the alternating direction method of multipliers (ADMM). Finally, comprehensive experiments on TC128, OTB2013, OTB2015 and UAV123 benchmarks demonstrate that the STAR tracker achieves compelling performance compared with the state-of-the-art (SOTA) trackers.

scholarly journals Distractor-Aware Deep Regression for Visual Tracking

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 387 ◽  
Author(s):  
Ming Du ◽  
Yan Ding ◽  
Xiuyun Meng ◽  
Hua-Liang Wei ◽  
Yifan Zhao
Keyword(s):  
Object Tracking ◽  
Visual Tracking ◽  
Test Data ◽  
Loss Function ◽  
State Of The Art ◽  
Target Object ◽  
Visual Object ◽  
Visual Object Tracking ◽  
Training Samples ◽  
Better Than

In recent years, regression trackers have drawn increasing attention in the visual-object tracking community due to their favorable performance and easy implementation. The tracker algorithms directly learn mapping from dense samples around the target object to Gaussian-like soft labels. However, in many real applications, when applied to test data, the extreme imbalanced distribution of training samples usually hinders the robustness and accuracy of regression trackers. In this paper, we propose a novel effective distractor-aware loss function to balance this issue by highlighting the significant domain and by severely penalizing the pure background. In addition, we introduce a full differentiable hierarchy-normalized concatenation connection to exploit abstractions across multiple convolutional layers. Extensive experiments were conducted on five challenging benchmark-tracking datasets, that is, OTB-13, OTB-15, TC-128, UAV-123, and VOT17. The experimental results are promising and show that the proposed tracker performs much better than nearly all the compared state-of-the-art approaches.

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